728 results about "Fuzz testing" patented technology

The present application provides a vulnerability detection method, comprising: acquiring a target branch of a target API in a detected code; generating a test case of the target API on the basis of proof information of a vulnerability API, wherein the test case of the target API is used for testing whether the target API has a vulnerability; determining an original seed for performing fuzz testing on the target branch of the target API; inputting the original seed into the test case of the target API; testing the target branch by means of the test case into which the original seed is inputted; screening for a seed on the basis of a test result; mutating the screened seed, and then inputting the mutated seed into the test case of the target API; and performing the next round of testing on the target branch by means of the test case into which the mutated seed is inputted so as to detect the vulnerability in the detected code. According to the method, saturation fuzz testing is performed on a specific branch (i.e., the target branch) of a specific API in a detected code, thereby achieving the objective of triggering the vulnerability quickly, realizing efficient fuzz testing for a vulnerability discovery scenario, and improving the vulnerability discovery efficiency.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

The invention relates to a fuzzy testing method, device and equipment based on a large language model, and the method comprises the steps: analyzing and analyzing RFC standard documents of various network protocols through the large language model, and extracting protocol rules and constraint conditions; according to the protocol rule and the constraint condition, a large language model driving method is adopted to generate a test case conforming to protocol characteristics; and performing a differential test through the test case, and verifying the consistency between the actual software of the network protocol and the RFC standard document. According to the invention, the accuracy and security of implementation of the complex network protocol can be improved, and the test efficiency and coverage rate are optimized.

The invention provides an automatic feedback method based on a protocol fuzz test and related equipment. The method comprises the following steps: acquiring a dynamic configuration file of the protocol fuzz test; performing real-time monitoring on the protocol fuzz test according to the protocol characteristic parameters of the dynamic configuration file, and determining a real-time data stream of the protocol fuzz test; performing multi-level feature analysis on the real-time data stream to judge an abnormal event existing in the protocol fuzz test; generating a feedback instruction corresponding to the abnormal event by performing multi-dimensional correlation analysis based on a knowledge graph on the abnormal event; and sending an exception notification mail to the user terminal according to the feedback instruction. Through an automatic mail notification mechanism, it is ensured that a tester can receive a notification immediately when a test task fails, the workload of manual monitoring is reduced, and the test efficiency is improved.

The invention provides a large-model-assisted program function automatic perception fuzzy test method and system, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a source code and a description document of a to-be-tested program; analyzing the source code and the description document based on the first large model to obtain all functional parameters supported by the to-be-tested program; identifying a dependency relationship and a constraint condition among the functional parameters based on the source code and the extracted functional parameters; executing a fuzzy test based on the dependency relationship between the function parameters and the constraint condition; and automatically analyzing the test result of the fuzzy test based on the second large model to generate a structured report. According to the method, the coverage rate and effectiveness of fuzzy testing can be optimized, and the quality and safety of a complex software system are comprehensively guaranteed.

The invention discloses a network protocol fuzz testing method and device based on state guidance and seed variation, and the method comprises the steps: 1), manually constructing a high-quality basic seed according to an official RFC protocol document, and enabling the seed to be mapped with a corresponding state; 2) inputting the message sequence into a target program for fuzzy testing, interacting with the target program, monitoring a program state, and obtaining feedback; 3) according to the protocol state code and path coverage rate feedback, selecting seeds with high coverage rate to carry out information retention variation, and reserving the state conversion function of the seeds to reduce the number of invalid seeds; and 4) generating a state guide message sequence: selecting a target test state according to a current state and a returned protocol state code, preferentially selecting a state which is not reached or has few arrival times as a target, and then generating a message sequence capable of reaching or approaching the target state according to the basic seed. According to the method provided by the invention, the state information of the protocol is fully utilized, the state conversion capability of the seed is reserved in the seed variation process, and the efficiency and the quality of the network protocol fuzz test are improved through state guidance and targeted message sequence generation.

The invention relates to a fuzzy test Kubernete-based three-party component vulnerability mining method, which comprises the following steps of: firstly, automatically identifying a user controllable function by combining a large language model based on an official document, a configuration list and source code information of a three-party component, and constructing a keyword dictionary to assist in large-scale identification of a potential target function; then analyzing context information of the target function and an interaction relation of the target function in a Kubernetes cluster, constructing a front dependency library required by fuzzy testing, and automatically generating a fuzzy testing driving program; integrating a fuzzy test driving program with a three-party component source code, performing code instrumentation, pre-compiling and corpus optimization, generating test input according to a set variation strategy, dynamically monitoring a coverage rate and an abnormal behavior, and continuously optimizing a test corpus; and finally, based on the crash behavior of the fuzzy test record, analyzing a trigger path of the vulnerability, performing vulnerability verification in a local Kubernetes cluster environment, and finally generating a vulnerability analysis report.

The invention belongs to the technical field of software engineering, and particularly relates to a project dependency library vulnerability step-by-step utilization method based on LLM enhancement. The method comprises the following steps: carrying out lightweight dependency tree and call graph reachability analysis on a project, and identifying all reachability call chains from the project to a vulnerability method; decomposing the fuzz test on the whole call chain into a directional fuzz test on each step of method call in the call chain; in combination with static analysis and prompt engineering technologies, seed generation is assisted by semantic understanding of LLM on a current test function and utilization information generated by a previous test. And performing directional fuzzy testing by utilizing the generated seeds, analyzing execution paths of the seeds through an execution feedback mechanism, guiding selection and variation of the seeds, and finally generating effective input capable of successfully utilizing the project dependent library vulnerabilities. According to the method, developers can be helped to quickly position and timely repair vulnerabilities, so that risks and potential loss of projects caused by dependency on the vulnerabilities are reduced.

The invention belongs to the field of fuzz testing, and discloses an industrial control protocol fuzz testing method and system based on a large language model, and the method comprises the steps: obtaining a protocol document, a to-be-tested protocol code, and a protocol sample; analyzing the protocol document based on a large language model to obtain protocol analysis information; performing vulnerability extraction on the protocol sample with the vulnerability information based on a large language model to obtain a plurality of vulnerability documents; based on the retrieval enhancement generation framework, retrieving an external knowledge base by adopting the query statement and the protocol analysis information to generate an enriched initial seed set; performing code auditing on the protocol code to be tested based on the large language model to obtain a vulnerability code of the protocol code, and performing instrumentation on the vulnerability code of the protocol code to obtain an instrumented code; mutation is conducted on the input seeds based on a preset seed energy distribution rule, and fuzzy testing is conducted on the instrumentation codes based on the input seeds subjected to mutation. According to the invention, the probability and speed of generating abnormity in fuzzy testing are improved.

Embodiments of the present disclosure provide a fuzz testing method, an electronic device, and a computer readable storage medium. The fuzz testing method comprises: acquiring a data packet; generating a protocol rule tree model on the basis of the data packet; generating a test case on the basis of the protocol rule tree model; sending the test case to a device under test; and determining a test result on the basis of response information of the device under test.

The invention focuses on the field of fuzz testing, and particularly relates to intelligent auxiliary fuzz testing drive code generation by using a large language model. The invention provides a convenient, fast and automatic fuzz test driving code generation method for Python library code fuzz test by using a large language model and combining a test function and a sample code. The method comprises the steps that Python library source code analysis and drive code sample information extraction are conducted through a static analysis tool, and a data set of a test function and a sample code is obtained; constructing a prompt word engineering structure comprising a drive sample code, a generation task description, a variation strategy introduction, a test function information set, a drive sample code and a code standardization description, and generating zero-sample, less-sample and thinking chain prompts according to the number of samples; building a large language model agent end, transmitting cue words to open-source and closed-source large language models in an HTTP / HTTPS (Hyper Text Transfer Protocol / Hyper Text Transfer Protocol Secure Socket) message mode, and performing reasoning generation of fuzzy test driving codes; after the drive codes are obtained, the fuzz test drive codes which do not pass the pilot run test are generated and screened for multiple rounds, and finally a fuzz test drive code set to be subjected to vulnerability mining is obtained; and performing vulnerability mining on the test function by utilizing the obtained fuzzy test driving code set, and storing a result to a database for analysis.

The invention discloses an automatic network protocol fuzz testing system, particularly relates to the field of automatic network protocol testing, is used for solving the problems of protocol vulnerability detection and security verification, and improves the coverage rate and efficiency of protocol testing through an automatic network protocol fuzz testing system. The method comprises the following steps: firstly, capturing and recording complete communication data, generating an FSA of a protocol, mapping a protocol state and a conversion process by utilizing a proxy server, and performing message clustering and state association analysis and simplifying protocol behavior analysis by generating a GMS; secondly, dynamically analyzing the stability of the protocol state and the difference of the message sequence, intelligently selecting a deterministic or non-deterministic test function, and widely covering a protocol behavior scene; in the testing process, input, output and state information is recorded in real time, and the protocol state is automatically reset or returned when abnormity occurs, so that the continuity and traceability of the test are ensured. The potential vulnerabilities can be found comprehensively, the manual test workload is reduced, and the security and robustness of the protocol are ensured.

The invention relates to an interactive visualization method, device and equipment for fuzzy test bottleneck breakthrough, and belongs to the field of network security. According to the method, all compiling modules in a tested program are combined into a complete intermediate code, and a global view of the tested program is obtained; generating a unique label for each function by using a hash algorithm; constructing a call graph and a control flow graph, and obtaining function call instruction information by scanning a basic block of each function; carrying out pile insertion on the basic blocks; performing a fuzzy test on the tested program after compiling and instrumentation, and obtaining runtime data in real time; according to the fuzzy test runtime data and the static analysis data, data-driven visualization is carried out, and a call graph, a control flow graph and real-time execution statistical information are displayed in real time; a test engineer is assisted through an interactive chart, and fuzzy test bottleneck constraints are positioned. According to the method, a test engineer can be helped to better understand and intervene the fuzzy test process, and the fuzzy test code coverage rate and the defect detection number are improved.

The invention provides an operating system kernel fuzz testing method, device and equipment, and relates to the technical field of kernel fuzz testing. The method comprises the following steps: acquiring initial input, and splitting the initial input to generate an input set; performing a fuzzy test on the input set and obtaining a fuzzy test result; storing the fuzzy test result into a key component inference file to generate a key component inference table; and generating a seed selection strategy in the seed bank in combination with the key component inference table so as to guide seed variation selection. By identifying and utilizing key components in a system calling sequence, the coverage rate of the fuzzy test is improved. And establishing a corresponding relationship between the key component in the system call sequence and the basic block of the corresponding path, so as to better mine potential security vulnerabilities. Different variation strategies are executed according to selection of variation points, so that an existing fuzzy testing method is effectively improved, and the vulnerability mining efficiency and quality are improved.

The invention relates to the technical field of network security and vulnerability detection, in particular to an intelligent contract vulnerability detection method and system based on hybrid fuzzy test optimization, and the method comprises the steps: carrying out the static analysis of compiled EVM byte codes, generating a first test case set, carrying out the fuzzy test, and introducing taint analysis to track the transmission of taints in an instruction stream; in the fuzzy testing process, according to the length change of the instruction counting set within the execution frequency threshold value, symbol execution processing is triggered when the length change value is 0, in the symbol execution processing process, a constraint cache reuse strategy is adopted to solve an obtained constraint expression, and a solving result is cached and reused, so that a fuzzy test result is obtained; a use case value reaching a complex branch can be quickly generated through symbolic execution and is fed back to the fuzzy engine to assist the fuzzy engine in exploring a new path to capture more deep vulnerabilities; and meanwhile, test predictions are designed for three intelligent contract vulnerabilities of authority control, transaction sequence dependence and denial of service, and vulnerability detection is more accurate.

The invention discloses a fuzzy testing method and device, equipment, a storage medium and a product, and the method comprises the steps: obtaining a target code, carrying out the static analysis of the target code, and determining the possible vulnerability information and vulnerability reachable path information of the target code; generating a test seed according to the possible vulnerability information and the vulnerability reachable path information; performing a fuzzy test on the target code through the test seed, determining vulnerabilities existing in the target code, and collecting dynamic feedback information in the fuzzy test process; and optimizing the test seed according to the dynamic feedback information, and returning to execute the step of performing the fuzzy test on the target code through the test seed until a preset test stop condition is met. According to the fuzzy test method disclosed by the invention, the dynamic feedback information is collected in each fuzzy test process, and the test seeds are optimized according to the dynamic feedback information, so that the test efficiency and accuracy can be improved, and finally efficient vulnerability detection and security evaluation are realized.

The invention relates to the technical field of data processing, and discloses a system kernel fuzzy test seed generation method and system based on a large language model. The method comprises the following steps: performing feature extraction on Linux kernel system calling on a Host host machine to obtain a feature data set; performing CFG analysis on the feature data set to obtain a system call sequence diagram and extracting a state transition table; constructing a test scene for the state transition table, and performing Syzkaller verification to obtain an effective scene library; generating an initial seed case set for the effective scene library, and executing a test in the QEMU to obtain result data; and performing coverage analysis on the result data to obtain a coverage rate report, and optimizing to generate a target test case set. According to the invention, the test coverage rate and the defect discovery capability are improved.

The invention discloses a kernel fuzz testing method and system based on a large language model, and belongs to the technical field of kernel fuzz testing. The method comprises the following steps: constructing a mapping relationship between system calling of a target operating system and a kernel object; based on the mapping relationship between the system call and the kernel object, generating a test case by using a large language model; and under the condition that the test case passes the verification, obtaining a fuzzy test result of the target operating system based on the test case. According to the method, the reasoning generalization ability of the LLM is utilized to generate a large number of effective system call sequences which conform to the dependency relationship and are wide in coverage range, so that the ability of a fuzzy test tool to generate test cases is enhanced, and potential vulnerabilities are mined.

The invention discloses a directional fuzzy testing method for wide adaptation of Windows platform application programs. The directional fuzzy testing method comprises the steps that target program adaptation is carried out; the target function meeting the condition is positioned, the execution path of the monitoring program is dynamically instrumented during running, information is recorded, and detailed intermediate files and dump files are generated; in the static analysis stage, functions related to input file operation are focused, a series of target function offsets are generated for a command line program and a part of GUI programs, and most valuable targets are screened according to the coverage condition of performance functions and the calling depth of file operation functions; identifying a target station; determining potential vulnerabilities and key execution paths before executing the directional fuzzy test; performing directional fuzzy testing; performing directional fuzzy testing on the target program; and fuzzy test optimization: improving the test efficiency by implementing a time slicing strategy. The method has the advantages of simple principle, easiness in implementation, good test effect and the like.

The invention relates to a protocol fuzz testing method and system based on a long-life-cycle enumeration variable state. The method comprises the following steps: S1, a static enumeration declaration statement instrumentation analysis stage; s2, a short life cycle enumeration variable dynamic analysis stage; s3, a static assignment statement instrumentation analysis stage; and S4, a fuzzy test stage. The system comprises a static analysis module, a dynamic analysis module, a network IO module and a variation feedback module. According to the method, the source code of the protocol entity program to be tested is statically analyzed to capture distribution and use of enumeration type variables in the source code. State transition is represented by monitoring changes of enumeration variable value domains, and a corresponding state tree structure is constructed, so that stateful grey box fuzz testing is carried out on any protocol entity program to be tested under the condition that extra manual analysis is not needed.

The invention provides a network protocol fuzz testing method based on strategy gradient reinforcement learning, and relates to the technical field of fuzz testing, and the method comprises the steps: building a test corpus through obtaining a to-be-tested protocol data packet format; establishing an action space containing a mutation operation set and a state space of a benchmark test data packet feature vector; determining a reward value based on the test result; calculating a strategy gradient by using a reward value, and updating strategy network parameters in combination with an adaptive learning rate; and performing fuzzy testing by using the trained model. According to the invention, the test efficiency is improved, the variation strategy selection is optimized, and the effectiveness of the network protocol security test is enhanced.

The present invention provides a fuzz testing system and method based on a virtual computer model. Traditional fuzz testing approaches primarily modify external inputs to detect security vulnerabilities, making it difficult to monitor internal state changes in real-time. This invention constructs a test environment using a virtual computer model and incorporates automatic assertion generation and detection functions, enabling precise analysis of both internal and external signals of the system. After executing fuzz testing, assertion data is analyzed to assess test coverage and identify untested code regions, allowing for an optimized testing strategy. This improves the detection rate of security vulnerabilities, enhances test automation and reliability, and can be applied in various fields such as networks, automotive ECUs, IoT, finance, aerospace, and semiconductors.

The invention discloses a fuzz testing method and system for code generation and automatic program repair based on a large model, which are used for solving the problems of high code generation error rate and high testing cost in the running process of an existing large model fuzzer. The method comprises four stages of cue word screening, fuzzy circulation, program repairing and vulnerability detection. A large language model is used as a main generator, an operator selection problem in a fuzzy loop stage is modeled as a multi-arm tiger machine problem, and a Thompson sampling algorithm is used to improve the diversity and effectiveness of program generation. The generated program is cyclically repaired, so that program errors caused by lack of API (Application Program Interface) combination constraints are reduced. According to the method, the effectiveness of the program generated by the fuzz test based on the large model is obviously improved, the problem that the error rate of the program generated by an existing fuzzer based on the large model is high is solved, and the code coverage rate is higher.

The invention discloses an industrial control protocol intelligent fuzzy test method and system based on a multi-agent large model, and relates to an industrial information security test method and system.According to the industrial control protocol intelligent fuzzy test method and system based on the multi-agent large model, the large model is enhanced by integrating the field of the retrieval enhancement generation (RAG) and the low-rank quantization adaptation (QLoRA) technology, and a multi-agent cooperation mechanism is combined; and full-automatic, high-precision and end-to-end fuzzy testing of the industrial protocol is realized. The system generates seed data meeting protocol specifications through the seed generation agent, the test case generation agent executes field, structure and semantic variation based on the seed data, efficient test cases are dynamically generated, a test strategy is optimized in real time through the feedback analysis and strategy adjustment agent, and the vulnerability discovery capability is improved. By dynamically adjusting the variation strategy and optimizing the generation of the test case, the coverage, the accuracy and the efficiency of the test are improved, and the security and the vulnerability detection capability of the industrial control system are remarkably improved.

The invention provides a distributed system load balancing test method and device based on load difference degree guidance. The method comprises the steps that multi-dimensional input is converted into a one-dimensional initial input operation sequence; wherein the multi-dimensional input comprises one or a combination of more of a working load, a load configuration and a node fault; generating an initial test case according to the initial input operation sequence, and mutating the selected initial test case to obtain a new test case; wherein the initial test case is stored in a seed pool; controlling the distributed system to operate the new test case, and calculating the load difference degree of each pair of nodes in the distributed system according to the load data of the nodes in the distributed system during operation; and determining a target test case according to the load difference degree, and storing the target test case in a seed pool to guide next fuzzy test iteration. According to the method, three-dimensional input of user requests, system configuration changes, system fault conditions and the like which influence a load balancing mechanism is modeled into a standard one-dimensional input sequence, so that subsequent fuzzy testing is facilitated, and the testing efficiency is improved; meanwhile, the load difference degree between the distributed nodes is used as guide feedback to optimize the input space, high-quality test cases can be continuously generated, load imbalance between the nodes in the distributed system is revealed to the maximum extent, developers can be helped to more effectively recognize and solve potential performance bottlenecks, and the test efficiency is improved. The test coverage rate is improved, and the overall stability and response speed of the system are also improved.

One example method for training a machine learning model using fuzz test data comprises: at a test system: performing, using test configuration information, a plurality of fuzz testing sessions involving one or more systems under test (SUT); obtaining fuzz test data from one or more sources, wherein the fuzz test data includes test traffic data and SUT performance data associated with the plurality of fuzz testing sessions; training, using the fuzz test data and one or more machine learning algorithms, a machine learning model for receiving as input traffic data involving a respective SUT and SUT performance data and providing as output a stress state value indicating the likelihood of the respective SUT crashing or failing; and storing, in a machine learning model data store, the trained machine learning model for subsequent use by the test system or a SUT analyzer.

The invention discloses a knowledge extraction and envelope coverage-oriented software vulnerability test method and related equipment, and the method comprises the steps: obtaining vulnerability key information of target software, and obtaining sensitive function features through defect dependence analysis and construction based on the vulnerability key information; performing semantic analysis on the analysis report and the code abstract associated with the sensitive function characteristics, screening to obtain a target sensitive function, performing path envelope reverse tracking on the target sensitive function, and constructing to obtain a target path envelope; and obtaining coverage information enveloped by the target path, and carrying out fuzzy testing on the basis of the coverage information in combination with the iteratively optimized variation sample. According to the method, through intelligent closed loop of analysis-positioning-testing-feedback-optimization, static analysis provides accurate guidance for dynamic testing, and the static analysis strategy is inversely optimized by the result of the dynamic testing, so that the maximum improvement of the testing efficiency and the vulnerability discovery accuracy is realized in limited testing resources, and the testing efficiency and the vulnerability discovery accuracy are improved. The method can be widely applied to the technical field of software security.

The invention belongs to the technical field of computer information processing, and particularly discloses an internet of things equipment firmware-oriented fuzz test input optimization device, which comprises a static analysis module used for carrying out firmware analysis through an API interface, identifying an input transmission point of bare computer firmware, and realizing automatic identification through a custom script; the dynamic simulation module is used for identifying an input transfer point of the firmware of the real-time operating system and realizing a length inference mechanism and a final input transfer algorithm; the input routing mapping module is used for identifying and dynamically monitoring a data retrieval point, an input processing point and an availability check instruction in firmware to obtain a mapping result; the input transmission point identification module is used for automatically identifying an input transmission point of the firmware based on the mapping result; and the sensing module is used for dynamically determining the minimum length and the maximum length of the input data of the input transmission point, optimizing the input transmission point of the firmware fuzz test, and dynamically calculating the length of the reserved data. According to the invention, the fuzz testing efficiency can be improved.

The invention discloses an aero-engine compiler fuzzy test method and system based on a large language model. In a variation prompt generation stage, a seed program library is constructed by using embedded programs of various structures and compiler test suite programs, variation operators are selected through diversified guide strategies, and variation prompts are constructed based on a prompt template. In the test generation stage, a large language model is used for generating a variant program with complex data flow and control flow so as to enhance the test coverage rate of a compiler; global and local variables are monitored by inserting test output for the program, so that the program can effectively find out silent miscompilation errors with the maximum harm; in addition, a front-end error feedback strategy is designed, so that the effectiveness of a large language model generation test program is improved. In the differential testing stage, random difference testing and testing under different optimization levels are combined, so that the defects of the middle and rear ends of the compiler are detected. According to the method, the coverage rate of compiler testing can be increased, and silent miscompilation errors can be detected.

The present invention discloses an optimization method for industrial control protocol fuzz testing based on coverage guidance. This method combines mutation testing on the basis of traditional coverage-guided fuzz testing. By comparing the output results of the program under test and the mutated PUT, high-quality test cases are screened. At the same time, the concept of coverage-guided tracking is introduced. By encoding the current coverage boundary in the binary file of the program under test, self-reporting can be performed when new coverage is generated in the test case, eliminating unnecessary tracking of the coverage-guided fuzzer, improving the fuzz testing efficiency, and greatly enhancing the vulnerability detection ability and testing efficiency of coverage-guided fuzz testing.